linkedin-post-generator▌
casper-studios/casper-marketplace · updated Apr 8, 2026
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Generate LinkedIn posts from shared source material, written in each user's personal style.
LinkedIn Post Generator
Generate LinkedIn posts from shared source material, written in each user's personal style.
How It Works
- Personal style profile — stored locally at
~/.config/casper/linkedin-style.md(never committed) - Source config — stored locally at
~/.config/casper/linkedin-sources.md(never committed) - Shared source material — meeting transcripts, Slack dumps, docs in
source-material/ - Prompt template — extraction rules, voice guidelines, few-shot examples in
references/prompt-template.md
First Run: Style Setup
Check if ~/.config/casper/linkedin-style.md exists.
If it does NOT exist, run the style setup flow:
- Say: "Welcome to the LinkedIn Post Generator! Before we start, I need to understand your writing style."
- Say: "Share 3 LinkedIn posts that match the style you want. You can either paste the post links (e.g.
https://linkedin.com/posts/...) or paste the text directly." - Wait for the user to provide 3 posts
- If the user provides LinkedIn URLs, fetch the post content using the apify-scrapers skill:
Extract the post text from the JSON output. If a URL fails to fetch, ask the user to paste that post's text instead.python ${CLAUDE_PLUGIN_ROOT}/skills/apify-scrapers/scripts/scrape_linkedin_posts.py search "{url}" --max-posts 1 - Analyze the posts for: tone, sentence length, vocabulary, formatting habits, hook style, CTA style, use of questions, paragraph length, overall energy
- Create
~/.config/casper/directory if it doesn't exist - Save the analysis to
~/.config/casper/linkedin-style.mdusing this format:
# LinkedIn Style Profile
Generated: [date]
## Tone
[analysis]
## Structure Patterns
[paragraph length, line breaks, formatting habits]
## Hook Style
[how they open posts]
## CTA / Closing Style
[how they end posts — questions, challenges, etc.]
## Vocabulary & Phrases
[distinctive phrases, word choices, energy level]
## Sample Posts
[the 3 original posts, for reference]
- Confirm: "Got it! Your style profile is saved. You can update it anytime with
/casper:generate-linkedin-post --setup"
First Run: Source Material Check
After style setup completes (or if style exists but source-material/ is empty), check for source material:
- Check if
source-material/contains any.mdfiles besidesREADME.md - If empty, guide the user:
- Say: "You don't have any source material yet. I need content to generate posts from — meeting transcripts, notes, Slack conversations, etc."
- Present options:
- "Connect integrations" — run the
--setup-sourcesflow to configure Fireflies, Slack, or Google Drive auto-pulling - "Paste something manually" — run the
--add-sourceflow to let the user paste a transcript, notes, or other content
- "Connect integrations" — run the
- Wait for user choice and proceed with the selected flow
- If source material exists, proceed with generation
Normal Run: Post Generation
If style config exists and source material is available, proceed with generation:
- Read
~/.config/casper/linkedin-style.md - Read ALL files in
${CLAUDE_PLUGIN_ROOT}/skills/linkedin-post-generator/source-material/(excluding README.md) - Read
${CLAUDE_PLUGIN_ROOT}/skills/linkedin-post-generator/references/prompt-template.md - Apply the confidentiality rules from the prompt template (no financials, no client names, no pipeline, no team member names)
- Generate 2-4 post options based on the source material, written in the user's personal style
- Present them in a clean, copy-paste-ready format
Flags
| Flag | Behavior |
|---|---|
| (none) | Normal generation flow |
--setup |
Re-run style setup, overwrite existing config |
--setup-sources |
Configure which Fireflies, Slack, and Drive sources to pull from |
--refresh |
Pull fresh source material from configured integrations, then generate |
--view-style |
Read and display ~/.config/casper/linkedin-style.md |
--view-sources |
List and summarize all files in source-material/ |
--add-source |
Prompt user to paste new content, save as new .md file in source-material/ |
Flag Details
--setup-sources
Interactive setup for automatic source pulling. Read references/source-integrations.md for full details.
- Ask: "What's your work email address? This is used to filter transcripts to only meetings you attended."
- Save as
user_emailin the config
- Save as
- Ask: "Which sources do you want to connect?" Present options:
- Fireflies.ai — pulls meeting transcripts (needs
FIREFLIES_API_KEYenv var) - Slack — pulls messages from channels (needs
SLACK_BOT_TOKENenv var) - Google Drive — pulls docs and transcripts (needs OAuth setup via google-workspace skill)
- Fireflies.ai — pulls meeting transcripts (needs
- For each selected source, check if the required env var / credentials exist. If missing, provide setup instructions:
- Fireflies: "Set
FIREFLIES_API_KEYin your environment. Get your API key from https://app.fireflies.ai/api" - Slack: "Set
SLACK_BOT_TOKENin your environment. Create a Slack app at https://api.slack.com/apps" - Google Drive: "Run the google-workspace skill setup to configure OAuth."
- Fireflies: "Set
- For each enabled source, gather configuration:
- Fireflies: search terms (or leave empty for all recent) and days_back
- Slack: which channels to pull from — list available channels if possible, otherwise ask the user
- Google Drive: search terms, days_back
- Save to
~/.config/casper/linkedin-sources.md - Confirm: "Source config saved. Run
/casper:generate-linkedin-post --refreshto pull fresh content."
--refresh
Pull fresh source material from all configured integrations before generating posts. Read references/source-integrations.md for the full integration workflow.
Summary of the flow:
- Read
~/.config/casper/linkedin-sources.md— if missing, run--setup-sourcesfirst - For each enabled source, call the existing Casper skill scripts:
- Fireflies:
python ${CLAUDE_PLUGIN_ROOT}/skills/transcript-search/scripts/fireflies_transcript_search.py "{term}" --days-back {N} --content --json- After fetching, filter results to only transcripts where
user_email(from source config) appears in the transcript'sparticipantsarray
- After fetching, filter results to only transcripts where
- Slack:
python ${CLAUDE_PLUGIN_ROOT}/skills/slack-automation/scripts/slack_search.py read "{channel}" --days {N} - Google Drive:
python ${CLAUDE_PLUGIN_ROOT}/skills/google-workspace/scripts/gdrive_search.py files "{term}" --modified-days {N} --json
- Fireflies:
- Convert JSON output to clean markdown and save to
source-material/:- Fireflies:
fireflies-{YYYY-MM-DD}-{title-slug}.md - Slack:
slack-{channel}-{YYYY-MM-DD}.md - Google Drive:
gdrive-{title-slug}-{YYYY-MM-DD}.md
- Fireflies:
- Proceed with normal generation
--view-style
Read ~/.config/casper/linkedin-style.md and display it. If it doesn't exist, say "No style profile found. Run /casper:generate-linkedin-post --setup to create one."
--view-sources
List all .md files in ${CLAUDE_PLUGIN_ROOT}/skills/linkedin-post-generator/source-material/ (excluding README.md). For each file, show the filename and a 1-line summary of its contents.
--add-source
- Ask: "Paste the content you want to add as source material (meeting transcript, Slack dump, notes, etc.)"
- Ask: "What should I name this source file? (e.g.,
team-standup-jan-2025)" - Save as
${CLAUDE_PLUGIN_ROOT}/skills/linkedin-post-generator/source-material/[name].md - Confirm: "Source material saved. It will be included in future post generation."
Reference Files
| File | When to Read |
|---|---|
references/prompt-template.md |
Every generation run — contains voice rules, few-shot examples, confidentiality rules |
references/source-integrations.md |
When running --refresh or --setup-sources — contains script paths, arguments, output conversion |
references/style-setup.md |
When running --setup — contains analysis framework for style profiling |
source-material/*.md |
Every generation run — raw content to extract post ideas from |
How to use linkedin-post-generator on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add linkedin-post-generator
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches linkedin-post-generator from GitHub repository casper-studios/casper-marketplace and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate linkedin-post-generator. Access the skill through slash commands (e.g., /linkedin-post-generator) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
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Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★30 reviews- ★★★★★Zaid Desai· Dec 20, 2024
linkedin-post-generator fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Chaitanya Patil· Dec 12, 2024
We added linkedin-post-generator from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Naina Kapoor· Dec 4, 2024
I recommend linkedin-post-generator for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Naina Sharma· Nov 27, 2024
Useful defaults in linkedin-post-generator — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Meera Sethi· Nov 23, 2024
Keeps context tight: linkedin-post-generator is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Advait Thomas· Nov 11, 2024
We added linkedin-post-generator from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Piyush G· Nov 3, 2024
linkedin-post-generator fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Shikha Mishra· Oct 22, 2024
linkedin-post-generator is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Tariq Haddad· Oct 18, 2024
linkedin-post-generator has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Anaya Liu· Oct 14, 2024
Registry listing for linkedin-post-generator matched our evaluation — installs cleanly and behaves as described in the markdown.
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